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Implements some high level ideas from papers.

Not really functional. Can only emulate solutions proposed for large models due to resource limitation.

parallelism/

Virtual implementation of distributed training/serving parallelism. Virtual hardware are implemented as:

  • cluster.py
    • VirtualDevice: Emulates a compute device/machine using a multiprocessing.Process. Have all collective ops (e.g. AllScatter, AllGather, AllReduce, ReduceScatter, etc.) as builtin.
    • VirtualChannel: Emulates intra-device and host2device, device2host communication through multiprocessing.Queue.
    • VirtualCluster: Emulates a compute cluster through a pool of multiprocessing.Processs and multiprocessing.Queues.
  • mesh.py
    • Mesh: Hardware topology configuration.
    • MeshIndex: Hardware topology index.
  • operation.py
    • Operation: An operation to be executed by the compute cluster.
    • ShardedOperation: An operation to run w/ tensor parallelism.
    • PipelinedOperation: An operation to run w/ pipeline parallelism.
  • sharding.py
    • DimSharding: Sharding configuration along a dimension.
    • TensorSharding: Sharding configuration along a tensor.
  • pipelining.py
    • Pipeline: A pipeline configuration. Mostly including total number of concurrent batches, stages, and hardware mesh mapping.
    • PipelineStage: A pipeline stage configuration and status. Mostly including the status of which cycle this stage is running, and the associated helper functions to decide if it is in forward, backward, or idle status.

tensor_pallelsim/

Intra-op model parallelism through sharding tensors and computation.

  • matmul.py
    • MatMulSharding: Tensor parallelism sharding configuration.
    • MatMulShardingPolicy: Tensor parallelism sharding policies for function dispatch.
      • Unsharded: No sharding. Duplicated runs.
      • MatchedInnerSharding: Matched inner dimension sharding. Run with AllReduce.
      • UnmatchedInnerSharding: Unmatched inner dimension sharding. Run with AllGather.
      • OuterSharding: Outer (m) and inner (k) dimension sharding. Run with AllGather->AllGather.
      • FullSharding: All uter (m, n) and inner (k) dimension sharding. Run with AllGather->AllGather.
    • MatMul: Tensor parallelism based operation. Implementation is dispatched based on MatMulShardingPolicy.
  • ffn.py
    • FeedForwardSharding: Tensor parallelism sharding configuration.
    • FeedForwardShardingPolicy: Tensor parallelism sharding policies for function dispatch.
    • FeedForward: Tensor parallelism based operation. Implementation is dispatched based on FeedForwardShardingPolicy.

pipeline_paralleism/

Inter-op model parallelism through run different stages of the model asynchronously.

  • mlp.py
    • MLP: A simple MLP model with 4 dense layers running as different stages of pipeline. Both forward and backward paths are managed. Activations, parameters, gradients, and states are managed but optimizers are not.